Decisions In Social Networks Research Articles

Modeling joint evacuation decisions in social networks: The case of Hurricane Sandy

Coastal areas of the United States are vulnerable to substantial loss of lives and property damage from repeatedly occurring hurricanes and evacuation is the usual recourse to prevent loss of life when high storm surge threatens. The fundamental question in evacuation modeling is to explore the complex evacuation decision-making process leading to an individual’s decision to evacuate or not during a hurricane threat. Recent studies suggest that the social network characteristics of individuals could potentially determine overall evacuation patterns. This study explores the joint evacuation decisions of individuals in personal networks by using ego-centric social network data obtained from Hurricane Sandy and by considering the nested structure of the ego-centric network data, i.e. close contacts (alters) as nested within an individual (ego). In this regard, the study develops a multinomial multilevel model of joint evacuation decisions at the dyadic (ego-alter tie) level utilizing a Hierarchical Generalized Linear Modeling (HGLM) approach. Model estimation results suggest factors that define a social tie (contact frequency, discussion topic and geographic proximity) significantly influence the evacuation decisions between individuals and their social partners. In addition, individuals’ (both ego and alter) own socio-demographics such as age, marital status, previous evacuation experience, evacuation order, household’s type, size, location and proximity to a water body also affect the decision to evacuate. These findings are useful to help emergency managers implement efficient evacuation strategies and to facilitate planning by policymakers by determining fractions of people evacuating or not for a major hurricane within the context of their social networks.

Optimal Nonlinear Pricing in Social Networks Under Asymmetric Network Information

How should a firm make pricing decisions in social networks when the customers hold in private their local network information? In “Optimal Nonlinear Pricing in Social Networks Under Asymmetric Network Information,” Zhang and Chen develop a solution approach based on calculus of variations and positive neighbor affiliation to tackle this problem. They show that the optimal pricing compromises the capitalization of the susceptibility to neighbor consumption with the motivation of one’s own consumption, which gives rise to a menu of quantity premium or quantity discount. In the Erdös and Rényi graph (a special case of the social network model in this paper), they find that the pricing scheme does not screen network positions; consequently, the firm can offer a simple uniform price. The authors also find that, in the context of two-way connections, the firm-optimal consumption becomes linear in customer degree in the scale-free network.

Stochastic optimization on social networks with application to service pricing

In this paper we develop a combined simulation and optimization approach for solving difficult decision problems on complex dynamic networks. For a specific reference problem we consider a telecommunication service provider who offers a telecommunication service to a market with network effects. More particularly, the service consumption of an individual user depends on both idiosyncratic characteristics and the popularity of this service among the customer’s immediate neighborhood. Both the social network and the individual user preferences are largely heterogeneous and changing over time. In addition the service provider’s decisions are made in absence of perfect knowledge about user preferences. The service provider pursues the strategy of stimulating the demand by offering differentiated prices to the customers. For finding the optimal pricing we apply a stochastic quasi-gradient algorithm that is integrated with a simulation model that drives the evolution of the network and user preferences over time. We show that exploiting the social network structure and implementing differentiated pricing can substantially increase the revenues of a service provider operating on a social network. More generally, we show that stochastic gradient methods represent a powerful methodology for the optimization of decisions in social networks.